Data mapping is the transformation of data for adaptation to a certain usage. It is often necessary to define comprehensive data transformation mappings between complicated data structures in more than one place. Data mapping is the process of creating transformation between the elements of two distinct data scopes. Defining this transformation takes time and is quite error prone. Sometimes, graphical tools are used in order to show the relationships between data objects by drawing lines representing the connections between fields from different data scopes. Algorithms are used for creating the mapping automatically, based mainly on the idea of connecting the fields from the two scopes that have the same name. This is usually done when handling heterogeneous data, which makes the mapping a complex problem. There are technologies for overcoming this heterogeneous complexity for relational data sources. The structural heterogeneity is a basic mapping problem in order to find effective mappings between distinct data structures. Facilitating and automating data mapping is one of the fundamental challenges for data interoperability.
Data transformations in most cases are quite complex and it is a hard task to find errors during execution of the transformation or the reason why the actual result differs from the expected result. It is even impossible to find the reason of failure for non-mapping domain expert. Typically, a debugger operates with the source code, which defines the data transformation. Such approach requires deep and comprehensive technical and domain knowledge from the person who performs debugging. With visual debugging capabilities, a mapping domain expert will not be required to debug the problems. What happens during the execution of the transformation and what the actual transformation of the source into the target context is will be presented in a simple and clear way.